智能电网环境下用电行为异常检测的深度学习算法研究——以宁夏某工业园区配电网负荷优化工程为例  

Research on Deep Learning Algorithm for Anomaly Detection of Electricity Consumption Behavior in Smart Grid Environment——Taking the Distribution Network Load Optimization Engineering in an Industrial Park in Ningxia as an Example

作　　者：吕呈 LV Cheng(State Grid Yinchuan Power Supply Company,Yinchuan 750001,Ningxia,China)

机构地区：[1]国网银川供电公司,宁夏银川750001

出　　处：《光源与照明》2025年第2期97-99,共3页Lamps & Lighting

摘　　要：文章以宁夏某工业园区配电网负荷优化工程为例,探索了深度学习技术在用电行为异常检测中的应用,设计了一套完整的深度学习检测框架,包括数据采集与特征处理、深度学习网络建模、异常检测算法优化及系统集成与边缘部署。研究结果表明,基于深度学习的用电行为异常检测技术能显著提高电网运行效率,为智能电网的精准管理提供有效技术支撑,并在分布式电网场景下具有广泛推广意义。Taking the distribution network load optimization engineering in an industrial park in Ningxia as an example,this paper explores the application of deep learning technology in the anomaly detection of electricity consumption behavior,designs a complete set of deep learning detection framework,including data acquisition and feature processing,deep learning network modeling,anomaly detection algorithm optimization,system integration and edge deployment.The research results show that the anomaly detection technology of electricity consumption behavior based on deep learning can signifi cantly improve the operation efficiency of power grid,provide effective technical support for the precise management of smart grid,and has wide popularization signifi cance in distributed power grid scenarios.

关 键 词：智能电网 用电行为 异常检测 深度学习算法 

分 类 号：TM76[电气工程—电力系统及自动化]